Thesis Defense Announcement

The College of Arts and Sciences announces the Final Thesis Defense of

Craig Kelly

for the Degree of Master of Science

November 5, 2018 at 12:00 PM in Dunn Hall, Room 311

Advisor: Deepak Venugopal 

Parallel Adaptive Collapsed Gibbs Sampling

ABSTRACT: Rao-Blackwellisation is a technique that provably improves the performance of Gibbs sampling by summing-out variables from the PGM. However, collapsing variables is computationally expensive, since it changes the PGM structure introducing factors whose size is dependent upon the Markov blanket of the variable. Therefore, collapsing out several variables jointly is typically intractable in arbitrary PGM structures. In this paper, we propose an adaptive approach for Rao-Blackwellisation, where we add parallel Markov chains defined over different collapsed PGM structures. The collapsed variables are chosen based on their convergence diagnostics. Adding chains requires us to re-burn-in the chain, thus wasting samples. To address this, we initialize the new chains from a mean field approximation for the distribution, that improves over time, thus reducing the burn-in period. Our experiments on several UAI benchmarks shows that our approach is more accurate than state-of-the-art inference systems such as Merlin that implements algorithms that have previously won the UAI inference challenge.